Modern indoor location-based services (ILBS) have rapidly expanded into people's daily life. Predicted by Market and Markets, indoor location market will worth 2.6 Billion U.S. dollars by 2018. To meet the breath of the golden opportunity, several major cooperations have initiated their researches on indoor localization, such as Apple, Google, Microsoft, Nokia, etc. The competition mainly focuses on the location accuracy and user experience.
With the proliferation of wireless communication and mobile computing, WLAN advances indoor localization with its prevalent infrastructure and low cost, as compared with multiple short range communication technologies like infrared, ultrasonic, RFID, and Zigbee sensors. So nowadays, WLAN has become one of the most popular positioning techniques. In WLAN, positioning systems consist of several fixed access points (APs) and an object with a WiFi-enabled device (e.g., wireless router, laptop or smartphone). The fixed AP, also known as static AP, is an infrastructure having a fixed location and a fixed working frequency. The nomadic AP is a WiFi-enabled device having a mobile location. The mainstream principle of WLAN-based positioning system is to leverage propagation models or location fingerprints with the topology of the fixed APs to estimate the target object's location.
However, the above-described WLAN-based indoor positioning systems still suffer from a serious problem named “spatial localizability variance”. That is, while the overall performance of the positioning system is stable, the localization accuracy at certain places is in low resolution. As a result, it brings in user experience inconsistency and leads to a poor user experience. One typical example can be found in location-based advertising in a large marketplace. Normally, an appropriate advertisement for a specific customer is chosen based on the statistics of the customer's current location and his history data. However, if the location accuracy is in low resolution, the statistic data can be misleading and may thus lead to inappropriate advertising. The reason for “spatial localizability variance” is the fixed APs are not dedicated for localization functionality. In addition, the dynamic change of the indoor environment, such as the movement of people and the movement of equipments, can affect the localization. Therefore, finding a way to optimize the topology of fixed APs to reduce the effects caused by dynamic deployment of fixed APs will be very helpful to indoor localization.
Existing WLAN-based positioning methods can be classified into two categories, i.e. propagation model-based method and fingerprint-based method. The propagation model-based methods calculate the distance between the transmitter and the receiver, estimate the object's location by trilateration or multilateration. The fingerprint-based methods use wireless devices to collect signals from reference positions as fingerprints, pre-process the fingerprint and save them to a database, in this way, by matching the received signal with the data in database, the object's location can be determined. However, since the propagation-based methods need calibration to obtain the environment parameters, the fingerprint-based methods cannot establish the location fingerprint database due to the mobility of nomadic APs, both of these two methods are not fit for solving the problem of spatial localizability variance.